Real-time decision-making has become a standard for many organizations or, at the least, a long-term goal to strive for. Streaming SQL, one way to enable real-time decision-making, is a fundamental component of any modern data architecture where real-time decisions are a factor in staying competitive. Many companies now rely on their ever-increasing flow of data to make data-driven decisions regardless of where the data resides. Streaming SQL is an essential piece in real-time data processing solutions that can help to unlock next-level growth. Instead of waiting for data to land in its designated resting place, data can be analyzed instantly as it is being streamed.
Before now, databases were where organizations stored data and query it when needed. The data needed to get into the database before meaningful queries could be executed on it. With the continuous production of data, it is now important to extract value and insights from the data immediately after it is generated. If the ability to execute queries has to wait until the data has made it into the database, the data may be stale by the time it is queried. This is the nature of the new world of fast-paced data creation.
Streaming SQL, a SQL-like language that can be used to query data streams as they are being produced, is able to bridge this gap. It is different from the original version of SQL because it differs in how it handles stored and real-time data.
Streaming SQL has been adopted by popular open-source streaming processors like Kafka Streams, Storm, Flink, Spark Streaming, and Samza. They now support this technique to offer users the opportunities that come with using Streaming SQL. This widespread adoption has also allowed the technology to be available to the cloud, real-time applications, IoT, Big Data, and other platforms with ease.
What is Streaming SQL?
To get a clearer understanding of Streaming SQL, let us look at what streaming and SQL stand for before combining them to form a concrete definition.
Streaming (Event Streams)
In today’s world, data is always in motion as more and more infrastructures are built to constantly interact with one another automatically. This has created a need for many solutions to cater to this and offer some type of streaming functionality within their platforms. Streaming, or event streams, refer to data flows that are generated continuously and used by applications that can handle these streams of data. Platforms such as Apache Kafka, Hadoop, Pulsar, and Kinesis were formed to handle streams of data where users can create, collect, store, and process data in motion with streams of events. Processing this data while it is in motion is generally referred to as stream processing. Event streams and stream processing can be used for many different types of interactions occurring within applications, servers, IoT, and traditional databases.
Structure Query Language (SQL) is a language that is used to query traditional Relational Database Management Systems (RDMS). SQL typically queries data “at rest” that is found in tables of databases. With the continuous and staggering growth of data over the years, there is an increased demand for analysis of data on the go while data is in motion. Analyzing data in real time requires an adaptation of SQL.
In this context, there is a need for a new version of SQL that supports stream processing. This has ushered forth the birth of Streaming SQL. Streaming SQL is similar to traditional SQL but it varies in syntax because of how it addresses storage and real-time data.
From the definition of streams and SQL, we can deduce that Streaming SQL is an alternative query language that can be used for real-time data. Streaming SQL supports stream processing by constantly receiving flows of data and allowing queries to be executed against it. Streaming SQL has a different language abstraction and query semantics for handling real-time data in motion and at rest. Compared to traditional SQL, it uses materialized views, windows, and event tables to keep data updated by triggering actions when changes occur in the data.
Differences Between SQL On Streams And Databases
In this section, we will look at the differences between SQL on streams and in traditional databases to help you understand the concept better.
Point-in-time vs Continuous Queries
Traditional databases are designed to store data in tables. Querying these tables can answer questions about the state of the data at that point in time when the table was queried. Running an SQL query on a traditional database provides a static response of the state of the data at a single point in time. For example, retrieving an account balance is a function of a point-in-time query. Querying for the balance of an account returns the exact balance on the account but does not say how that balance was calculated or arrived at.
The limitations of traditional SQL can be overcome by using continuous queries. Stream events store individual data records (events) as an ordered sequence by providing a historical view of how the data has changed. This historical view shows what happened and adds references and context to event tables from files, databases, and other sources. So in the case of the account balance scenario outlined above, with Streaming SQL you not only get the balance but also, how it arrived at the balance. Users would be able to see account transactions like debits or credits made and the tables the transactions were made from. One of the strengths of Streaming SQL is its ability to transform, filter, aggregate, enrich data, and combine all these features. Using Streaming SQL enables users to come up with valuable deductions from their data that is not possible with traditional SQL queries.
Response Time vs Lag
In traditional databases, you query the database and wait for it to run the query and compute the results before getting your response. This amount of time it takes to complete a query is referred to as “response time”. Response times can measure in milliseconds or can take several minutes depending on what was queried. When querying streams, you do not have to worry about this as you get a response when there is a change in the data. This responsiveness, being able to view changes in the data instantly, is because all data is in the materialized view. Just like traditional SQL, there are times when query responses may be delayed. In the case of Streaming SQL, if there is an increase in the event input, it leads to a time lag. Time lag refers to the amount of time the output is trailing the input in streaming systems. This means there may be times when a query output can have a slight delay or lag between when the data changed and when the actual query results were returned back to the user or system.
Different Actions Create Work For The Underlying Engine
If a traditional database engine is not running an operation or query, the database will sit, idling until it needs to run an operation. While the database is idle, the data is passive in the store. Data sits at rest in the database and the database engine only comes alive when a user issues a query to add or retrieve data. When a user tries to run a query, the engine starts planning and optimizing the query and will later run the query to provide results. After it does this and returns a result or completes an operation, it goes back to being idle again. If a query has been returned and the data in the database changes, the query will need to be reissued in order to get an updated version of the result.
The approach above, though useful in some scenarios, cannot cater to the demands of today’s organizations and their demand for real-time data. Organizations require constant interaction with data for improved productivity and to unlock use cases where constant data streaming is required. This requires a change of approach where the database executes queries as data changes and does this every time new data arrives. This approach is needed to keep query results fresh, rather than being idle until queried like a traditional database.
The ability to keep query results updated is made possible by using Streaming SQL. Each newly arriving data record causes the query to immediately react and process the information. Once the data is refreshed, the data changes can be shared and propagate throughout the entire system concurrently. With this method, you do not have to re-run the query to get an updated result like the traditional database; rather, you get an updated version in real-time, as the data changes.
As the volume of data being produced increases, it simply becomes inefficient to constantly query and re-query the database as data changes occur. This “polling”-style approach to keeping data up to date can put unnecessary strain on systems, especially when no data has changed. Running streaming queries is a more efficient and natural fit when trying to keep queries updated in real-time. When using streaming queries, the database performs stream processing in which results are executed immediately and constantly, one change event or record at a time.
Why is Streaming SQL Useful?
Streaming SQL can be extremely useful for use cases where data is constantly changing and/or real-time data updates, without polling a data source, are required. Streaming SQL allows updates in the data to be “pushed” out to the query result instead of having queries constantly rerun to “pull” results back if any. Let’s look at a few scenarios where Streaming SQL is advantageous.
Data is Important When It First Appears
With Streaming SQL, data is processed as it arrives. This allows users or systems using the data to make meaning of it for immediate use. This analysis and querying of the data, while it is in motion, is very useful and important for lots of organizations as it allows for faster and more accurate decision-making. Decisions and analytics made with real-time data are more reliable and relevant. Leveraging the data as close to its moment of creation as possible is the best way to pull valuable insights and action them. Streaming SQL facilitates this type of use of data.
SQL is Helpful In Deriving Insights From Streaming Data
SQL is a powerful language that allows us to query a database to answer questions about our data. The paradigm shift of doing this using streaming queries to interact with data can help in deriving insights from the data while it is still in motion. The advantage of this design helps us to derive maximum value from our continuous data production. This is done by emitting changes, and refreshing the query result, whenever a change or an event takes place in the data.
The example below shows the difference between traditional SQL and Streaming SQL queries. The query finding results from a table of customers where the customers are in the specified country. The difference between the two queries is the addition of the Streaming SQL EMIT CHANGES clause in the second query.
From the illustration above, with traditional SQL you will have to query it any time you want to see if there is a change in the table. Streaming SQL provides you with this information every time a change occurs and it will keep on running until it is explicitly terminated. Depending on the use case, this could be much more efficient than regular SQL, which terminates once the result is returned.
What Are The Examples of Streaming SQL?
Seeing Streaming SQL in action is a great way to uncover potential use cases for it within your organization. Let’s take a look at some high-level use cases that show the capabilities of Streaming SQL.
Business Intelligence and Analytics
When trying to get the most from your data analysis to ensure your company’s growth and make intelligent business decisions, real-time data matters. Streaming SQL is a useful option to help you in achieving real-time decision-making capabilities. Streaming SQL can also be used as a data pipeline. This approach overcomes the issue of loading data in various time intervals or using batch processing. It can also solve issues that arise from caching, denormalization, and bugs that cause outages.
Using Streaming SQL eliminates the need to use code for data ingestion and transformation in microservices. Any component of the microservice that uses a recurring query in its code can be easily replaced with Streaming SQL and have changes streamed directly as they happen.
Streaming SQL is a must if you want to build or operate a real-time application. With Streaming SQL, queries are highly effective at making it easy for organizations to collect, process, and deliver real-time data across various environments. This is true for both cloud and on-premise applications.
With Streaming SQL, you can build applications that utilize real-time analytics and query results. This opens up use cases where real-time where real-time data streams can be used for customer-facing and internal business capabilities. One such example would be to use Streaming SQL to power real-time dashboards where you can see instant changes to your data to monitor operations and make more reliable and informed decisions.
This blog post has covered some important aspects of Streaming SQL. It discussed how the SQL language can be enhanced to support a continuous data stream. We dove into what Streaming SQL is, the difference between traditional SQL and Streaming SQL, and looked at some areas where it can be used to your organization's advantage.
Building Streaming SQL to integrate your data into your analytics engine in real-time can be cumbersome. This is where the use of a platform which supports different data sources and destinations can help make things easier. Arcion, a data integration platform, has a wide range of data management capabilities and can be used as a solution for streaming data. To find out more about streaming real-time data, download Arcion Self-hosted for free today (no payment info required), and enable real-time pipelines in minutes.